1 Overview

1.1 Participants

A total of 1146 participants were recruited through a survey posted on Prolific. 184 were excluded as they did not complete the survey, and 98 were excluded as they are vegan/vegetarian, and 5 were excluded for indicating that their results should not be included in the analysis. 13 were excluded for failing to select the correct response in an attention check. The final sample (N = 846) ranged in age 18 to 79 (Mage = 37.21, SD = 13.58). The participants were predominantly female (56.58%). The participants received £0.35 ($0.45) for successfully completing the task.

1.2 Randomization check

A preliminary randomization check was conducted. The check revealed no systematic differences between the three conditions in gender, age, political position, and nationality (all p’s > .05).

Table 1.1: Randomisation check
Item Dynamic Static No norm Significance test
Age (years) 37.34 \(\pm\) 14.22 37.90 \(\pm\) 12.97 36.40 \(\pm\) 13.55 \(F(2, 843) = 0.89\), \(\mathit{MSE} = 184.49\), \(p = .409\)
Gender (%) 1 (39.49%) 2 (60.14%) 3 (0.36%) 1 (41.9%) 2 (58.1%) 1 (48.25%) 2 (51.05%) 3 (0.7%) \(\chi^2(4, n = 846) = 6.92\), \(p = .140\)
Political position 3.47 \(\pm\) 1.22 3.54 \(\pm\) 1.26 3.45 \(\pm\) 1.26 \(F(2, 843) = 0.39\), \(\mathit{MSE} = 1.55\), \(p = .679\)
Nationality (%) 1 (84.06%) 2 (3.62%) 3 (9.78%) 4 (0.72%) NA (1.81%) 1 (80.99%) 2 (5.28%) 3 (8.45%) 4 (1.41%) NA (3.87%) 1 (80.77%) 2 (5.94%) 3 (8.74%) 4 (1.75%) NA (2.8%) \(\chi^2(6, n = 846) = 3.23\), \(p = .779\)

1.3 Correlations

Table 1.2: Means, Standard Deviations, Reliabilities, and Inter-Correlations Among Study Measures
Alpha M SD 1 2 3 4 5 6
Interest
3.58 1.82
Attitude 0.90 4.64 1.29 .80**
Intention 0.98 4.22 1.80 .83** .82**
Expectation 0.99 3.93 1.75 .80** .80** .92**
Intent/expectation composite
4.08 1.74 .83** .83** .98** .98**
Perception of change
5.14 0.90 .27** .26** .27** .25** .27**
Preconformity
4.18 1.20 .44** .40** .39** .37** .39** .37**

2 Confirmatory analyses

2.1 1. Does making dynamic norms about reduced meat consumption in the UK salient lead to higher interest in reducing their meat consumption (compared to static norm)?

2.1.1 Effect of condition on interest in reducing meat consumption

Sparkman and Walton (2017) found effects of dynamic norms on interest in reducing meat consumption ranging from Mdiff = 0.60 – 0.78. Thus, the rough mean difference between dynamic and static norms expected in the sample is 0.69 on a 7 point Likert scale. Thus, I modeled H1 as a half-normal with an SD of 0.69. The plausible maximum effect was set at 1.38.

World cloud of participants response to text

Figure 2.1: World cloud of participants response to text

Table 2.1:
Most frequent words in text
word freq
health 216.00
environment 193.00
animals 144.00
climate 67.00
awareness 64.00
concerns 64.00
impact 56.00
media 50.00
vegan 43.00
better 35.00
change 33.00
vegetarian 32.00
production 31.00
bad 30.00
benefits 30.00
becoming 28.00
food 28.00
reduce 28.00
diet 27.00
cost 26.00

The mean interest for participants in the dynamic norm condition was M = 3.64 (SD = 1.83), and the mean interest in the static norm condition was M = 3.68 (SD = 1.84). The mean interest in the no norm condition was M = 3.41 (SD = 1.77).

There was no difference in interest in reducing meat consumption between the dynamic norm (M = 3.64, SD = 1.83) and static norm (M = 3.68, SD = 1.84) conditions, \(\Delta M = -0.03\), 95% CI \([-0.34\), \(0.27]\), \(t(843) = -0.23\), \(p = .821\), d = -0.02, \(B_{\text{HN}(0, 0.69)}\) = 0.18, RR[0.65, 2].

Participants in the no-norm control condition showed the least interest in reducing meat consumption (M = 3.41, SD = 1.77) and did not differ from those in the dynamic-norm condition \(\Delta M = 0.23\), 95% CI \([-0.07\), \(0.53]\), \(t(843) = 1.52\), \(p = .130\), d = 0.13, or the static-norm condition \(\Delta M = 0.27\), 95% CI \([-0.03\), \(0.57]\), \(t(843) = 1.76\), \(p = .080\), d = 0.15. There was also no difference between the dynamic-norm condition and a combination of the control and static-norm conditions \(\Delta M = 0.10\), 95% CI \([-0.16\), \(0.36]\), \(t(843) = 0.74\), \(p = .458\).

Table 2.2:
Meat consumption by condition contrasts
contrast \(\Delta M\) 95% CI \(t(843)\) \(p\)
DYST Dynamic, static -0.03 \([-0.34\), \(0.27]\) -0.23 .821
DYNO Dynamic, control 0.23 \([-0.07\), \(0.53]\) 1.52 .130
STNO Static, control 0.27 \([-0.03\), \(0.57]\) 1.76 .080
DYCONT Dynamic, both 0.10 \([-0.16\), \(0.36]\) 0.74 .458
EXPNO Norms, control -0.25 \([-0.51\), \(0.01]\) -1.89 .059

2.1.2 Effect of demographic variables and condition on interest

Political left-wing participants were more interested than were right wing participants, \(t(844) = -7.23\), \(p < .001\), and women were more interested than were men, \(\Delta M = -0.50\), 95% CI \([-0.75\), \(-0.26]\), \(t(781.22) = -4.00\), \(p < .001\). When we controlled for these factors, the effect of the dynamic-norm condition (compared with that of the static-norm condition) on interest in eating less meat was \(b = -0.27\), 95% CI \([-1.25\), \(0.72]\), \(t(553) = -0.53\), \(p = .594\).

Table 2.3:
Regression coefficients of demographic variables on interest
predictor \(b\) 95% CI \(t(553)\) \(p\)
Intercept 4.61 \([3.90\), \(5.31]\) 12.80 < .001
Condition -0.27 \([-1.25\), \(0.72]\) -0.53 .594
Gender 0.53 \([0.10\), \(0.96]\) 2.43 .015
Political position -0.37 \([-0.54\), \(-0.19]\) -4.14 < .001
Condition \(\times\) Gender -0.01 \([-0.61\), \(0.59]\) -0.03 .976
Condition \(\times\) Political position 0.09 \([-0.15\), \(0.33]\) 0.77 .443

2.2 2. Will participants in the dynamic norm condition be more likely (than static norm and control) to predict a future decrease in meat consumption in the UK?

I modeled H2 using a half-normal distribution with a mean of 0 and SD of Mdiff = 0.40. The plausible maximum effect was set at twice the predicted effect of Mdiff = 0.80. A Bayes factor was calculated for each test.

Table 2.4: Expectations of future meat consumption
Future Norm
Preconformity
Combined
Condition \(n\) \(M\) \(SD\) \(M\) \(SD\) \(M\) \(SD\)
Dynamic 276 5.26 0.93 4.35 1.18 4.81 0.88
Static 284 5.20 0.85 4.24 1.19 4.72 0.85
No norm 286 4.97 0.89 3.95 1.18 4.46 0.84

2.2.0.1 Measure of perception of change: “In the next 5 years, I expect meat consumption in the UK to…”

There was no evidence one way or another for an effect of dynamic norm condition on expectations about future meat consumption, \(\Delta M = 0.06\), 95% CI \([-0.08\), \(0.21]\), \(t(843) = 0.85\), \(p = .397\), d = 0.07, \(B_{\text{HN}(0, 0.40)}\) = 0.42, RR[0.05, 0.8]

Table 2.5: Perception change contrasts
contrast estimate ci statistic p.value
DYST Dynamic, static 0.06 \([-0.08\), \(0.21]\) 0.85 .397
DYNO Dynamic, control 0.30 \([0.15\), \(0.44]\) 3.94 < .001
STNO Static, control 0.23 \([0.09\), \(0.38]\) 3.11 .002
DYCONT Dynamic, both 0.18 \([0.05\), \(0.31]\) 2.75 .006
EXPNO Norms, control -0.26 \([-0.39\), \(-0.14]\) -4.08 < .001

2.2.0.2 Measure of preconformity: “In the foreseeable future, to what extent do you think that many people will make an effort to eat less meat?”

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (M = 4.35, SD = 1.18) and static norm (M = 4.24, SD = 1.19) conditions, \(\Delta M = 0.11\), 95% CI \([-0.09\), \(0.31]\), \(t(843) = 1.08\), \(p = .279\), d = 0.09, \(B_{\text{HN}(0, 0.40)}\) = 0.72, RR[0.05, 1.5].

Table 2.6: Preconformity contrasts
contrast estimate ci statistic p.value
DYST Dynamic, static 0.11 \([-0.09\), \(0.31]\) 1.08 .279
DYNO Dynamic, control 0.40 \([0.20\), \(0.60]\) 4.00 < .001
STNO Static, control 0.29 \([0.10\), \(0.49]\) 2.94 .003
DYCONT Dynamic, both 0.25 \([0.08\), \(0.43]\) 2.93 .004
EXPNO Norms, control -0.35 \([-0.52\), \(-0.18]\) -4.02 < .001

2.2.0.3 Combined

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (M = 4.81, SD = 0.88) and static norm (M = 4.72, SD = 0.85) conditions, \(\Delta M = 0.09\), 95% CI \([-0.06\), \(0.23]\), \(t(843) = 1.19\), \(p = .235\), d = 0.10, \(B_{\text{HN}(0, 0.40)}\) = 0.62, RR[0.05, 1.25].

Table 2.7: Combined contrasts
contrast estimate ci statistic p.value
DYST Dynamic, static 0.09 \([-0.06\), \(0.23]\) 1.19 .235
DYNO Dynamic, control 0.35 \([0.21\), \(0.49]\) 4.81 < .001
STNO Static, control 0.26 \([0.12\), \(0.40]\) 3.65 < .001
DYCONT Dynamic, both 0.22 \([0.09\), \(0.34]\) 3.45 .001
EXPNO Norms, control -0.31 \([-0.43\), \(-0.18]\) -4.89 < .001

3 Secondary analyses

3.1 1. Will there be a difference in perceptions of current static norm across the dynamic and static norm conditions?

The SESOI for percentage difference is ± 5%. The SESOI for mean difference on the Likert scale is ± 0.5.

TOST results: t-value lower bound: 129.959 p-value lower bound: 0e+00 t-value upper bound: \(-23.608\) p-value upper bound: \(2.99\times10^{-86}\) degrees of freedom : 557.66

Equivalence bounds (raw scores): low eqbound: \(-5\) high eqbound: 5

TOST confidence interval: lower bound 90% CI: 3.355 upper bound 90% CI: 3.57

NHST confidence interval: lower bound 95% CI: 3.335 upper bound 95% CI: 3.591

Equivalence Test Result: The equivalence test was significant, t(557.66) = \(-23.608\), p = \(2.99\times10^{-86}\), given equivalence bounds of \(-5\) and 5 (on a raw scale) and an alpha of 0.05. Null Hypothesis Test Result: The null hypothesis test was significant, t(557.66) = 53.175, p = \(1.51\times10^{-220}\), given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically different from zero and statistically equivalent to zero.

3.1.1 2. Will there be a difference in how meat consumption is construed across the dynamic and static norm conditions?

The SESOI for difference in number of meals is ± 2 meals.

TOST results: t-value lower bound: 15.086 p-value lower bound: \(1.12\times10^{-43}\) t-value upper bound: \(-13.542\) p-value upper bound: \(1.25\times10^{-36}\) degrees of freedom : 557.55

Equivalence bounds (raw scores): low eqbound: \(-5\) high eqbound: 5

TOST confidence interval: lower bound 90% CI: \(-0.306\) upper bound 90% CI: 0.845

NHST confidence interval: lower bound 95% CI: \(-0.417\) upper bound 95% CI: 0.956

Equivalence Test Result: The equivalence test was significant, t(557.55) = \(-13.542\), p = \(1.25\times10^{-36}\), given equivalence bounds of \(-5\) and 5 (on a raw scale) and an alpha of 0.05. Null Hypothesis Test Result: The null hypothesis test was non-significant, t(557.55) = 0.772, p = .441, given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically not different from zero and statistically equivalent to zero.

4 Exploratory Analyses

4.1 3. Does dynamic norm (versus static or no norm) information increase participants’ positive attitude, intentions, and expectations to reduce their meat consumption?

4.1.1 Trace plots

Traceplots of regression parameters

Figure 4.1: Traceplots of regression parameters

Traceplots of regression parameters

Figure 4.2: Traceplots of regression parameters

Traceplots of regression parameters

Figure 4.3: Traceplots of regression parameters

4.1.2 Posterior plots

Posterior uncertainty intervals

Figure 4.4: Posterior uncertainty intervals

Posterior density plot

Figure 4.5: Posterior density plot

4.1.3 Summary table

Table 4.1: Posterior results for simple model (H3)
Model 1: Uninformative priors\(^a\)
Model 2: Weakly informative priors\(^b\)
Model 3: Moderately informative priors\(^c\)
Mean (SD) 95% PPI neff PSRF Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior
Interest 0.040 (0.156) -0.265, 0.349 819.155 1.003 normal(0,10) 0.089 (0.154) -0.212, 0.388 771.276 1.005 122.5 normal(0.5,0.75) 0.196 (0.132) -0.059, 0.459 889.294 1.004 390 normal(0.5, 0.35)
Attitude -0.049 (0.111) -0.267, 0.165 771.073 1.004 normal(0,10) -0.012 (0.112) -0.234, 0.207 730.007 1.005 -75.51 normal(0.5,0.75) 0.064 (0.093) -0.117, 0.244 928.977 1.003 -230.61 normal(0.5, 0.35)
Intention/Expectation -0.036 (0.144) -0.314, 0.251 808.662 1.005 normal(0,10) 0.011 (0.145) -0.271, 0.291 725.346 1.006 -130.56 normal(0.5,0.75) 0.113 (0.122) -0.12, 0.353 931.226 1.004 -413.89 normal(0.5, 0.35)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .499 b ppp = .493 c ppp = .437

4.2 4. How do demographic factors such as age, gender, and political position predict primary dependent variables relating to meat consumption?

4.2.1 Trace plots

Traceplots for estimated regression parameters

Figure 4.6: Traceplots for estimated regression parameters

Traceplots for estimated regression parameters

Figure 4.7: Traceplots for estimated regression parameters

Traceplots for estimated regression parameters

Figure 4.8: Traceplots for estimated regression parameters

4.2.2 Posterior plots

Posterior uncertainty intervals

Figure 4.9: Posterior uncertainty intervals

Posterior density plot

Figure 4.10: Posterior density plot

4.2.3 Summary table

Table 4.2: Posterior results for full model (H4)
Model 1: Uninformative priors\(^a\)
Model 2: Informative priors\(^b\)
Model 3: Informative priors\(^c\)
Parameter Mean (SD) 95% PPI neff PSRF Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior
Interest
Condition 0.063 (0.147) -0.211, 0.354 1166.766 1 normal(0,10) 0.100 (0.142) -0.175, 0.372 1433.183 1.003 58.73 normal(0.5,0.75) 0.208 (0.130) -0.048, 0.475 1593.563 1 230.16 normal(0.5,0.35)
Age -0.002 (0.006) -0.013, 0.009 1873.321 1.001 normal(0,10) -0.002 (0.006) -0.013, 0.009 1905.213 1.001 0 normal(0,10) -0.002 (0.006) -0.014, 0.009 1954.115 0.999 0 normal(0,10)
Gender 0.524 (0.158) 0.204, 0.826 1197.542 1.001 normal(0,10) 0.525 (0.154) 0.224, 0.83 1055.009 1.002 0.19 normal(0,10) 0.524 (0.156) 0.213, 0.821 1167.231 1.004 0 normal(0,10)
Politics -0.308 (0.063) -0.43, -0.186 1210.214 1 normal(0,10) -0.311 (0.062) -0.43, -0.189 1240.268 1.004 0.97 normal(0,10) -0.312 (0.059) -0.425, -0.197 1361.417 1.003 1.3 normal(0,10)
Attitudes
Condition -0.033 (0.105) -0.233, 0.176 1163.076 1 normal(0,10) -0.008 (0.103) -0.21, 0.191 1381.633 1.003 -75.76 normal(0.5,0.75) 0.071 (0.094) -0.106, 0.26 1595.695 1.001 -315.15 normal(0.5,0.35)
Age 0.001 (0.004) -0.007, 0.009 2020.368 1 normal(0,10) 0.001 (0.004) -0.007, 0.009 2014.231 1.001 0 normal(0,10) 0.001 (0.004) -0.008, 0.009 1924.127 1 0 normal(0,10)
Gender 0.299 (0.112) 0.081, 0.518 1252.849 1.002 normal(0,10) 0.299 (0.110) 0.088, 0.511 1086.499 1.002 0 normal(0,10) 0.299 (0.112) 0.078, 0.513 1193.494 1.004 0 normal(0,10)
Politics -0.242 (0.045) -0.328, -0.152 1135.188 1.001 normal(0,10) -0.243 (0.045) -0.331, -0.156 1255.325 1.004 0.41 normal(0,10) -0.245 (0.043) -0.327, -0.16 1354.755 1.001 1.24 normal(0,10)
Intentions
Condition -0.018 (0.139) -0.278, 0.252 1159.325 1 normal(0,10) 0.014 (0.136) -0.255, 0.273 1470.680 1.003 -177.78 normal(0.5,0.75) 0.123 (0.124) -0.117, 0.369 1452.978 1.002 -783.33 normal(0.5,0.35)
Age 0.008 (0.005) -0.002, 0.018 1905.898 1.001 normal(0,10) 0.008 (0.005) -0.002, 0.019 1952.531 1.001 0 normal(0,10) 0.008 (0.005) -0.003, 0.018 1884.567 1 0 normal(0,10)
Gender 0.592 (0.148) 0.306, 0.88 1275.347 1.001 normal(0,10) 0.594 (0.145) 0.318, 0.881 1084.447 1.001 0.34 normal(0,10) 0.595 (0.147) 0.303, 0.874 1200.347 1.004 0.51 normal(0,10)
Politics -0.272 (0.060) -0.389, -0.156 1123.352 1.001 normal(0,10) -0.274 (0.059) -0.391, -0.158 1240.672 1.003 0.74 normal(0,10) -0.275 (0.057) -0.383, -0.161 1303.289 1.002 1.1 normal(0,10)
Expectations
Condition 0.063 (0.147) -0.211, 0.354 1166.766 1 normal(0,10) 0.100 (0.142) -0.175, 0.372 1433.183 1.003 58.73 normal(0.5,0.75) 3.148 (0.130) -0.048, 0.475 1593.563 1 230.16 normal(0.5,0.35)
Age -0.002 (0.006) -0.013, 0.009 1873.321 1.001 normal(0,10) -0.002 (0.006) -0.013, 0.009 1905.213 1.001 0 normal(0,10) 1.614 (0.006) -0.014, 0.009 1954.115 0.999 0 normal(0,10)
Gender 0.524 (0.158) 0.204, 0.826 1197.542 1.001 normal(0,10) 0.525 (0.154) 0.224, 0.83 1055.009 1.002 0.19 normal(0,10) 2.823 (0.156) 0.213, 0.821 1167.231 1.004 0 normal(0,10)
Politics -0.308 (0.063) -0.43, -0.186 1210.214 1 normal(0,10) -0.311 (0.062) -0.43, -0.189 1240.268 1.004 0.97 normal(0,10) 1.786 (0.059) -0.425, -0.197 1361.417 1.003 1.3 normal(0,10)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .501 b ppp = .508 c ppp = .457

4.3 5. How does age interact with norm condition to influence dependent variables?

4.3.1 Trace plots

Traceplots of regression parameters

Figure 4.11: Traceplots of regression parameters

Traceplots of regression parameters

Figure 4.12: Traceplots of regression parameters

Traceplots of regression parameters

Figure 4.13: Traceplots of regression parameters

4.3.2 Summary table

Table 4.3: Posterior results for multi-sample analysis by age (H4)
Model 1: Uninformative priors\(^a\)
Model 2: Informative priors\(^b\)
Model 3: Informative priors\(^c\)
Parameter Mean (SD) 95% PPI neff PSRF Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior
Older
Interest 0.127 (0.223) -0.307, 0.553 917.266 1.002 normal(0,10) 0.200 (0.206) -0.206, 0.601 930.554 1.001 57.48 normal(0.5,0.75) 0.362 (0.172) 0.041, 0.705 939.531 1.006 185.04 normal(0.5,0.35)
Attitude 0.035 (0.162) -0.276, 0.361 978.182 1.004 normal(0,10) 0.086 (0.149) -0.198, 0.375 961.888 1 145.71 normal(0.5,0.75) 0.208 (0.129) -0.037, 0.463 933.096 1.01 494.29 normal(0.5,0.35)
Intention -0.072 (0.214) -0.508, 0.35 849.457 1.004 normal(0,10) 0.004 (0.194) -0.376, 0.381 896.109 1 -105.56 normal(0.5,0.75) 0.170 (0.163) -0.156, 0.489 902.042 1.008 -336.11 normal(0.5,0.35)
Expectation 0.027 (0.224) -0.417, 0.456 838.891 1.002 normal(0,10) 0.120 (0.202) -0.275, 0.508 867.831 1.002 344.44 normal(0.5,0.75) 0.288 (0.164) -0.025, 0.606 963.833 1.003 966.67 normal(0.5,0.35)
Younger
Interest -0.086 (0.160) -0.4, 0.222 863.224 1.001 normal(0,10) -0.019 (0.148) -0.308, 0.271 910.991 1.002 -77.91 normal(0.5,0.75) 0.103 (0.120) -0.135, 0.334 891.487 1.002 -219.77 normal(0.5,0.35)
Attitude -0.012 (0.214) -0.435, 0.402 846.377 1.001 normal(0,10) 0.073 (0.193) -0.297, 0.457 891.293 1.002 -708.33 normal(0.5,0.75) 0.233 (0.157) -0.075, 0.532 866.660 1.004 -2041.67 normal(0.5,0.35)
Intention 0.127 (0.223) -0.307, 0.553 917.266 1.002 normal(0,10) 0.200 (0.206) -0.206, 0.601 930.554 1.001 57.48 normal(0.5,0.75) 0.362 (0.172) 0.041, 0.705 939.531 1.006 185.04 normal(0.5,0.35)
Expectation 0.035 (0.162) -0.276, 0.361 978.182 1.004 normal(0,10) 0.086 (0.149) -0.198, 0.375 961.888 1 145.71 normal(0.5,0.75) 0.208 (0.129) -0.037, 0.463 933.096 1.01 494.29 normal(0.5,0.35)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .480 b ppp = .494 c ppp = .415

5 Environment and data

5.0.1 Session information

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.0.4 (2021-02-15)
##  os       macOS Big Sur 10.16         
##  system   x86_64, darwin17.0          
##  ui       X11                         
##  language (EN)                        
##  collate  en_GB.UTF-8                 
##  ctype    en_GB.UTF-8                 
##  tz       Europe/London               
##  date     2021-04-24                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package      * version    date       lib source                        
##  abind          1.4-5      2016-07-21 [1] CRAN (R 4.0.2)                
##  assertthat     0.2.1      2019-03-21 [1] CRAN (R 4.0.2)                
##  backports      1.2.1      2020-12-09 [1] CRAN (R 4.0.2)                
##  bayesplot      1.8.0      2021-01-10 [1] CRAN (R 4.0.2)                
##  bfrr         * 0.0.0.9000 2020-10-07 [1] Github (debruine/bfrr@9b80a99)
##  bitops         1.0-6      2013-08-17 [1] CRAN (R 4.0.2)                
##  bookdown       0.21       2020-10-13 [1] CRAN (R 4.0.2)                
##  boot           1.3-27     2021-02-12 [1] CRAN (R 4.0.2)                
##  broom          0.7.6      2021-04-05 [1] CRAN (R 4.0.2)                
##  bslib          0.2.4      2021-01-25 [1] CRAN (R 4.0.2)                
##  cachem         1.0.4      2021-02-13 [1] CRAN (R 4.0.2)                
##  callr          3.7.0      2021-04-20 [1] CRAN (R 4.0.4)                
##  car            3.0-10     2020-09-29 [1] CRAN (R 4.0.2)                
##  carData        3.0-4      2020-05-22 [1] CRAN (R 4.0.2)                
##  cellranger     1.1.0      2016-07-27 [1] CRAN (R 4.0.2)                
##  cli            2.4.0      2021-04-05 [1] CRAN (R 4.0.2)                
##  coda           0.19-4     2020-09-30 [1] CRAN (R 4.0.2)                
##  codebook     * 0.9.2      2020-06-06 [1] CRAN (R 4.0.2)                
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##  curl           4.3        2019-12-02 [1] CRAN (R 4.0.1)                
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##  DBI            1.1.1      2021-01-15 [1] CRAN (R 4.0.2)                
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##  ggsignif       0.6.1      2021-02-23 [1] CRAN (R 4.0.2)                
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##  papaja       * 0.1.0.9997 2020-08-11 [1] Github (crsh/papaja@0457653)  
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##  R6             2.5.0      2020-10-28 [1] CRAN (R 4.0.2)                
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##  rio            0.5.26     2021-03-01 [1] CRAN (R 4.0.2)                
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##  xml2           1.3.2      2020-04-23 [1] CRAN (R 4.0.2)                
##  xtable         1.8-4      2019-04-21 [1] CRAN (R 4.0.2)                
##  yaml           2.2.1      2020-02-01 [1] CRAN (R 4.0.2)                
##  zip            2.1.1      2020-08-27 [1] CRAN (R 4.0.2)                
## 
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library

5.0.2 Codebook

5.0.3 Metadata

5.0.3.1 Description

Dataset name: results

The dataset has N=846 rows and 37 columns. 0 rows have no missing values on any column.

Metadata for search engines
  • Date published: 2021-04-24
x
DYUK
STUK
UKNATION
RESIDENT
GENDER
condition
conditionbi
genderbi
agebi
NATIONALITY
INTEREST
ATT1
ATT2
ATT3
INTENT1
INTENT2
INTENT3
EXPECT1
EXPECT2
EXPECT3
PERCEPTNUM
PERCEPTSCALE
CONSTRUAL_1
PERCEPTCHANGE
PRECONFORMITY
POLITICS
AGE
attitude_mean
intention_mean
expect_mean
expintent_avg
age_cent
PERCEPTCHANGE_r
comb_future
No norm
Static
Dynamic

5.1 Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "results",
  "datePublished": "2021-04-24",
  "description": "The dataset has N=846 rows and 37 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name            |label | n_missing|\n|:---------------|:-----|---------:|\n|DYUK            |NA    |       570|\n|STUK            |NA    |       562|\n|UKNATION        |NA    |        24|\n|RESIDENT        |NA    |         0|\n|GENDER          |NA    |         0|\n|condition       |NA    |         0|\n|conditionbi     |NA    |       286|\n|genderbi        |NA    |         3|\n|agebi           |NA    |         0|\n|NATIONALITY     |NA    |         0|\n|INTEREST        |NA    |         0|\n|ATT1            |NA    |         0|\n|ATT2            |NA    |         0|\n|ATT3            |NA    |         0|\n|INTENT1         |NA    |         0|\n|INTENT2         |NA    |         0|\n|INTENT3         |NA    |         0|\n|EXPECT1         |NA    |         0|\n|EXPECT2         |NA    |         0|\n|EXPECT3         |NA    |         0|\n|PERCEPTNUM      |NA    |         0|\n|PERCEPTSCALE    |NA    |         0|\n|CONSTRUAL_1     |NA    |         0|\n|PERCEPTCHANGE   |NA    |         0|\n|PRECONFORMITY   |NA    |         0|\n|POLITICS        |NA    |         0|\n|AGE             |NA    |         0|\n|attitude_mean   |NA    |         0|\n|intention_mean  |NA    |         0|\n|expect_mean     |NA    |         0|\n|expintent_avg   |NA    |         0|\n|age_cent        |NA    |         0|\n|PERCEPTCHANGE_r |NA    |         0|\n|comb_future     |NA    |         0|\n|No norm         |NA    |         0|\n|Static          |NA    |         0|\n|Dynamic         |NA    |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "keywords": ["DYUK", "STUK", "UKNATION", "RESIDENT", "GENDER", "condition", "conditionbi", "genderbi", "agebi", "NATIONALITY", "INTEREST", "ATT1", "ATT2", "ATT3", "INTENT1", "INTENT2", "INTENT3", "EXPECT1", "EXPECT2", "EXPECT3", "PERCEPTNUM", "PERCEPTSCALE", "CONSTRUAL_1", "PERCEPTCHANGE", "PRECONFORMITY", "POLITICS", "AGE", "attitude_mean", "intention_mean", "expect_mean", "expintent_avg", "age_cent", "PERCEPTCHANGE_r", "comb_future", "No norm", "Static", "Dynamic"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "DYUK",
      "description": "Dynamic UK",
      "@type": "propertyValue"
    },
    {
      "name": "STUK",
      "description": "Static UK",
      "@type": "propertyValue"
    },
    {
      "name": "UKNATION",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
      "@type": "propertyValue"
    },
    {
      "name": "RESIDENT",
      "value": "1. 1,\n2. 2",
      "@type": "propertyValue"
    },
    {
      "name": "GENDER",
      "value": "1. 1,\n2. 2,\n3. 3",
      "@type": "propertyValue"
    },
    {
      "name": "condition",
      "value": "1. Dynamic,\n2. Static,\n3. No norm",
      "@type": "propertyValue"
    },
    {
      "name": "conditionbi",
      "value": "1. Dynamic,\n2. Static",
      "@type": "propertyValue"
    },
    {
      "name": "genderbi",
      "value": "1. 1,\n2. 2",
      "@type": "propertyValue"
    },
    {
      "name": "agebi",
      "value": "1. younger,\n2. older",
      "@type": "propertyValue"
    },
    {
      "name": "NATIONALITY",
      "description": "Nationality",
      "@type": "propertyValue"
    },
    {
      "name": "INTEREST",
      "@type": "propertyValue"
    },
    {
      "name": "ATT1",
      "@type": "propertyValue"
    },
    {
      "name": "ATT2",
      "@type": "propertyValue"
    },
    {
      "name": "ATT3",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT1",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT2",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT3",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT1",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT2",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT3",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTNUM",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTSCALE",
      "@type": "propertyValue"
    },
    {
      "name": "CONSTRUAL_1",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTCHANGE",
      "@type": "propertyValue"
    },
    {
      "name": "PRECONFORMITY",
      "@type": "propertyValue"
    },
    {
      "name": "POLITICS",
      "@type": "propertyValue"
    },
    {
      "name": "AGE",
      "@type": "propertyValue"
    },
    {
      "name": "attitude_mean",
      "@type": "propertyValue"
    },
    {
      "name": "intention_mean",
      "@type": "propertyValue"
    },
    {
      "name": "expect_mean",
      "@type": "propertyValue"
    },
    {
      "name": "expintent_avg",
      "@type": "propertyValue"
    },
    {
      "name": "age_cent",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTCHANGE_r",
      "@type": "propertyValue"
    },
    {
      "name": "comb_future",
      "@type": "propertyValue"
    },
    {
      "name": "No norm",
      "@type": "propertyValue"
    },
    {
      "name": "Static",
      "@type": "propertyValue"
    },
    {
      "name": "Dynamic",
      "@type": "propertyValue"
    }
  ]
}`